A super resolution technology is known as an example of a technology to generate the restored image from the blurred image. When a learning based super resolution technology in the super resolution technology is used, a case example in which a low resolution image is associated with a high resolution image is learned and a result of the learning is used as a dictionary. One example of the learning based super resolution technology is described in non-patent document 1.
In the learning based super resolution technology described in non-patent document 1, the following process (hereinafter, referred to as a super-resolution process) is performed. First, the super-resolution process receives an input image that is a low resolution image.
Next, the super-resolution process generates a temporary high resolution image by increasing the number of pixels of the input image by using an interpolation method.
Further, the super-resolution process generates a low frequency component by subtracting the image obtained by increasing the number of pixels of the input image by using a nearest neighbor interpolation method from the temporary high resolution image.
The super-resolution process cuts out a low frequency patch from the generated low frequency component and calculates a low frequency feature quantity from the low frequency patch.
Next, the super-resolution process searches for some low frequency feature quantity learning data in a dictionary in order of increasing distance from the calculated low frequency feature quantity and reads a high frequency feature quantity paired with these data.
The super-resolution process selects one high frequency feature quantity on the basis of a distance at the time of the search, a consistency with an adjacent high frequency block, a co-occurrence probability of the low frequency feature quantity and the high frequency feature quantity separately learned at a learning stage, or the like.
The technology described in non-patent document 1 uses a dictionary structure with one-to-many relation in which the low frequency feature quantities that are mutually similar to each other are aggregated to one representative and whereby a memory amount is suppressed and a calculation cost is reduced.
An example of the learning technology to create the dictionary is described in patent document 1.
The technology described in patent document 1 includes the following functional means.
Specifically, it includes image input means which inputs an image, face detection means which detects a person's face image from the inputted image, first data collection means which collects a plurality of face data obtained from a plurality of face images in a plurality of images that are inputted and obtained by photographing the faces of a registrant taking various postures, second data collection means which collects a plurality of face data obtained from the plurality of the face images in the plurality of images that are inputted and obtained by photographing the face of the registrant during walking, integration means which integrates the face data collected by the first data collection means and the face data collected by the second data collection means, and storage means which stores the integrated data as dictionary data of the registrant.
In the technology described in patent document 1, the above-mentioned configuration is used. Therefore, a load on a user can be reduced and dictionary data can be efficiently registered.
Further, another related image processing technology is described in patent document 2.